The invention relates to patient monitoring and diagnostic devices, and particularly, to patient monitoring and diagnostic devices capable of acquiring multiple-lead electrocardiograph (ECG) signals and performing rhythm analysis on the signals.
It is commonly known in the art to provide patient monitoring and diagnostic devices, particularly multiple-lead capability ECG machines, with the ability to engage in complex ECG rhythm analysis. The rhythm analysis capability is usually a function of the software controlling the machine. Typically, the ECG waveform is filtered, and digitized, i.e., input to an analog-to-digital (A/D) converter. The digitized waveform is then analyzed by the rhythm analysis software. The goal of the rhythm analysis is to make an accurate diagnosis of the cardiac condition of the patient being evaluated.
It is estimated that approximately two to three percent of all patients evaluated using multiple-lead ECG machines have implanted pacemakers that assist the patient's cardiac performance. In order to provide an accurate rhythm analysis, the ECG machine must be able to detect the existence of pacemaker pulses in the ECG waveform and provide to the clinician an indication that the particular event on the ECG waveform is a pacemaker-triggered event, and not an abnormal physiological event, such as an ectopic heartbeat. Pacemaker pulses are typically seen as high frequency spikes on the ECG waveform. The timing relationship of detected pulses to features of the ECG is then used by the algorithm to determine the pacing mode of the implanted pacemaker.
Known patient monitoring and diagnostic systems perform pacemaker pulse detection prior to digitization of the ECG waveform. The detection is performed using analog signal processing techniques. In particular, a high pass filter is used to locate the presence of high frequency elements of the ECG waveform that indicate when a pacemaker pulse has occurred. The information about the detected pacemaker pulse is then passed on to the ECG analysis software by inserting artificial markers in the digitized ECG data wherever a pacemaker pulse is estimated to be. The analysis software then detects these markers and analyzes the ECG waveform with the knowledge that the event markers indicate pacemaker pulses.